[sottol]

Interesting, I find the exact opposite. Although to a much lesser extent (maybe 50% boost).

I ended shoehorned into backend dev in Ruby/Py/Java and don't find it improves my day to day a lot.

Specifically in C, it can bang out complicated but mostly common data-structures without fault where I would surely do one-off errors. I guess since I do C for hobby I tend to solve more interesting and complicated problems like generating a whole array of dynamic C-dispatchers from a UI-library spec in JSON that allows parsing and rendering a UI specified in YAML. Gemini pro even spat out a YAML-dialect parser after a few attempts/fixes.

Maybe it's a function of familiarity and problems you end using the AI for.

[freeone3000]

As in, it seems to be best at problems that you’re unfamiliar with in domains where you have trouble judging the quality?

[Brendinooo]

>it seems to be best at problems that you’re unfamiliar with

Yes.

>in domains where you have trouble judging the quality

Sure, possibly. Kind of like how you think the news is accurate until you read a story that's in your field.

But not necessarily. Might just be more "I don't know how do to <basic task> in <domain that I don't spend a lot of time in>", and LLMs are good at doing basic tasks.